Computers & Industrial Engineering最新文献

{"title":"Semiconductor probe card proactive maintenance using graph self-supervised learning and an empirical study","authors":"Tran Hong Van Nguyen ,&nbsp;Chen-Fu Chien","doi":"10.1016/j.cie.2025.110955","DOIUrl":"10.1016/j.cie.2025.110955","url":null,"abstract":"<div><div>Semiconductor probe cards for wafer testing are increasingly challenging owing to the high density of bonding pads on the surface of integrated circuits (IC), the growing complexity of IC device features, and the extensive customization required for various IC products. Early fault detection and resolution are crucial for proactive maintenance to maintain the optimal operational performance and overall equipment effectiveness of wafer probing test equipment. Furthermore, the increasing number of possible corrective solutions for probe cards and the lack of concatenated labels for model training, along with the heterogeneity of domain knowledge for similar problems, has made it increasingly difficult for the engineers to find effective solutions for abnormal symptoms. Most of the existing studies focus on fault diagnosis, failure detection and classification, and advanced equipment control. As part of a continuous effort to fill the gaps, this study aims to develop a UNISON framework for graph self-supervised learning that integrates knowledge graph (KG), graph convolutional neural networks (GCN), and self-supervised learning to predict the conditions and effectively recommend the optimal list of corrective actions for early detection and resolution of the detected abnormal symptoms. To validate the proposed approach, an empirical study was conducted in a leading semiconductor testing company in Taiwan. The results have shown practical viability of the developed solution that can effectively assist the engineers in selecting the corrective actions for proactive maintenance to reduce machine downtime and enhance customer satisfaction.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110955"},"PeriodicalIF":6.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A real-time A* algorithm for trajectories generation and collision avoidance in uncertain environments for assembly applications","authors":"Ahmed Nhouchi ,&nbsp;Salma Ben Said ,&nbsp;Mohamed Amine Ben Abdallah ,&nbsp;Nizar Aifaoui","doi":"10.1016/j.cie.2025.110959","DOIUrl":"10.1016/j.cie.2025.110959","url":null,"abstract":"<div><div>In assembly processes, generating paths and avoiding collisions are crucial for efficiency and safety. This paper presents a novel approach that integrates the A* pathfinding (PF) algorithm into FreeCAD, an open-source Computer-Aided Design (CAD) platform. The main contribution of this work is enabling PF and collision detection directly within the CAD environment during the design phase, helping detect potential collisions early and improving the design process. The A* algorithm has been adapted to handle both static and dynamic obstacles inside FreeCAD. This integration allows for better planning of paths in complex assembly environments. The integration process, algorithm modifications and system functionality are described in detail. A case study simulating an assembly line demonstrates the algorithm’s effectiveness in generating collision-free trajectories while adapting to dynamic changes in the environment. This work paves the way for further advancements in AI-driven CAD systems for industrial applications, enabling more intelligent and adaptive assembly processes during the design phase.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110959"},"PeriodicalIF":6.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized product design and user review and experience analysis: A data-driven hybrid novel approach","authors":"Shulin Lan ,&nbsp;Yinfei Jiang ,&nbsp;Tao Guo ,&nbsp;Shaochun Li ,&nbsp;Chen Yang ,&nbsp;T.C. Edwin Cheng ,&nbsp;Kanchana Sethanan ,&nbsp;Ming-Lang Tseng","doi":"10.1016/j.cie.2025.110939","DOIUrl":"10.1016/j.cie.2025.110939","url":null,"abstract":"<div><div>This study contributes to mass customization by addressing the lack of effective methods for extracting and analyzing personalized demand indicators from user feedback. Prior studies often neglect the mapping relationship between user feedback and production characteristics, the practical integration of user experience data with product design constraints, limiting their ability to meet diverse consumer needs. To overcome these challenges, this study proposes a data-driven approach that combines k-means clustering, sentiment analysis, and deep learning to identify key comment factors impacting the user experience of customized products. This study offers substantial scientific value by proposing a systematic and scalable method for understanding consumer preferences in mass customization. It provides manufacturers with actionable insights for improving product competitiveness and customer satisfaction. The results demonstrate that product thinness and performance are the most critical factors for personalized information technology product design, significantly influencing user satisfaction. Regression analysis confirms that while these factors, along with price, heavily affect user ratings, battery life and heat dissipation are of secondary importance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110939"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel utilités additives—based social network group decision-making method considering preference consistency","authors":"Zhiwei Xu ,&nbsp;Peng Li ,&nbsp;Cuiping Wei ,&nbsp;Jian Liu","doi":"10.1016/j.cie.2025.110947","DOIUrl":"10.1016/j.cie.2025.110947","url":null,"abstract":"<div><div>Nowadays, social networks and mobile internet have become prominent features of daily life, leading to increasingly interconnected relationships among decision-makers (DMs). The social network group decision-making (SNGDM) method uses social network analysis technology to consider the impact of social trust relationships among DMs on decision results during the decision-making process. The Utilités Additives (UTA) method can infer the DMs’ preference structure based on the partial preference information. This method effectively resolves the consensus problem in SNGDM by utilizing the DMs’ preference structure. This paper proposes a novel SNGDM method based on the UTA method that considers the consistency of preference information provided by DMs in the form of pairwise comparisons. Firstly, since the trust relationship between DMs is asymmetric and DM’s opinions are usually different, a new preference conflict degree between DMs in SNGDM is defined. Then, to consider the opinion differences and social trust relationship between DMs in the clustering process, a clustering method based on the preference conflict degree is proposed. Furthermore, to obtain the maximal subsets of consistent pairwise comparisons for each DM, we designed a simulation algorithm involving an optimization model to examine the pairwise comparisons provided by the DMs and to obtain the maximal subsets of consistent pairwise comparisons. Moreover, since using only preference information in the form of pairwise comparisons in the consensus reaching process (CRP) leads to a limited space for changes in DMs’ opinions, a method for converting the opinions of DMs based on maximal subsets of consistent pairwise comparisons is proposed. This method transforms pairwise comparisons provided by DMs into fuzzy preference relations (FPRs). In addition, in the CRP, a method for adjusting the FPRs of DMs is proposed. Finally, a case study is conducted using real data on new energy vehicles from Autohome to illustrate the effectiveness of the proposed method.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110947"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning enhanced swarm intelligence and trajectory-based algorithms for parallel machine scheduling problems","authors":"Fehmi Burcin Ozsoydan","doi":"10.1016/j.cie.2025.110948","DOIUrl":"10.1016/j.cie.2025.110948","url":null,"abstract":"<div><div>As one of the machine learning methods, reinforcement learning (RL) brings about notable novelties in a wide range of research fields. In several RL strategies, learning is carried out through an agent, a virtual entity that interacts with the environment. It is either rewarded or punished according to the consequences of the actions taken. These mechanisms can be employed as auxiliary procedures in numerous methods, such as metaheuristic algorithms, which are shown to have great potential for RL strategies. In this regard, this study introduces a two-staged approach for parallel machine scheduling problem (PMSP) with release times and sequence-dependent setup times, which has a large number of applications in real-life. The first stage in the proposed approach includes an RL-enhanced Particle Swarm Optimization (PSO) algorithm. The most notable contribution of this stage is that the proposed PSO is capable of self-tuning its parameters and coefficients according to system states. Therefore, any prior parameter setting or calibration is not necessarily required. In the second stage, the best-found solution by PSO is passed to an Iterated Greedy Search (IGS) algorithm, which is a distinguished trajectory-based metaheuristic algorithm. Thus, IGS attempts to further enhance the found initial solution by PSO. All obtained results in the comprehensive experimental study are verified via appropriate statistical tests. The outcomes of this study point out that the adopted RL contributes to the efficiency of the proposed approach for PMSPs. Secondarily, the canonical IGS can be considered a competitive algorithm due to its descent local search procedure.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110948"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A rapid population-based iterated greedy for distributed blocking group flowshop scheduling with delivery time windows under multiple processing time scenarios","authors":"Yizheng Wang ,&nbsp;Yuting Wang ,&nbsp;Yuyan Han ,&nbsp;Junqing Li ,&nbsp;Kaizhou Gao","doi":"10.1016/j.cie.2025.110949","DOIUrl":"10.1016/j.cie.2025.110949","url":null,"abstract":"<div><div>This paper addresses the distributed blocking flowshop group scheduling problem (DBFGSP) in multi-line cell manufacturing, considering uncertain processing times and delivery time windows. A mathematical model with robust optimization objective was constructed, and its correctness is validated using the Gurobi solver. To enhance computational efficiency, two rapid evaluation methods and a modified idle time insertion method were designed. Building on these methods, a population-based iterated greedy algorithm, <span><math><mrow><mi>R</mi><mi>A</mi><mi>P</mi><mi>_</mi><mi>P</mi><mi>I</mi><mi>G</mi><mi>_</mi><mi>I</mi><mi>T</mi><mi>I</mi></mrow></math></span>, was proposed. The algorithm was tested against four metaheuristic algorithms on 810 instances, demonstrating its superior performance in robust objective and relative deviation index (RDI) metrics.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110949"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scenario simulation and regulation policy optimization of industrial enterprise production water considering scale heterogeneity: A case study in the chemical industry","authors":"Dongying Sun ,&nbsp;Jialin Dong ,&nbsp;Xiaoya Gu ,&nbsp;Zhisong Chen","doi":"10.1016/j.cie.2025.110961","DOIUrl":"10.1016/j.cie.2025.110961","url":null,"abstract":"<div><div>To build a water-saving society, it is crucial to integrate the water-saving priority concept into the production and operation of enterprises and optimize the regulation policy. Hence, this study constructs a scenario simulation model for production water decision-making. Taking the chemical enterprises in Zhenjiang as an example, it investigates the response mechanism of water use decision-making of enterprises of different scales under designed scenarios comprising three policies (i.e., water price, subsidy, and penalty). Moreover, considering the government’s different preferences for environmental and economic benefits, the particle swarm optimization (PSO) algorithm is used to find the optimal combination of water-saving policies for enterprises of different scales. The results show that the response pattern of enterprises to changes in government water-saving regulation policies is heterogeneous by scale. Additionally, multiple policy combinations can better achieve the tradeoff between water-saving and economic benefits. The PSO can optimize the objective function composed of water-saving and economic profit under different weights. Considering the scale heterogeneity, it is more reasonable to formulate policies by scale than uniform policies. Finally, based on our findings, this study proposes targeted policy recommendations to promote water conservation in industrial enterprises.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110961"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of mathematical programming models and solution approaches for the textile supply chain","authors":"Giovanna Abreu Alves ,&nbsp;Roberto Tavares ,&nbsp;Pedro Amorim ,&nbsp;Victor Claudio Bento Camargo","doi":"10.1016/j.cie.2025.110937","DOIUrl":"10.1016/j.cie.2025.110937","url":null,"abstract":"<div><div>The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110937"},"PeriodicalIF":6.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimal effectiveness-driven target segment selection modeling approach for marketing campaign management","authors":"Cesar Salazar-Santander,&nbsp;Alejandro F. Mac Cawley,&nbsp;Carolina Martinez-Troncoso","doi":"10.1016/j.cie.2025.110945","DOIUrl":"10.1016/j.cie.2025.110945","url":null,"abstract":"<div><div>Defining a target group for a mass marketing campaign is a non-trivial goal, which depends on the correct definition of the commercial stimuli and the selection of a customer target segment that will maximize the campaign’s effectiveness. This process requires the analysis of multiple customer variables and interactions. The problem becomes even more complex if we consider a limited budget for the campaign. This research proposes a methodology based on a mixed multi-objective optimization formulation that allows us to determine a minimum continuous customer target segment for massive campaigns to maximize its effectiveness with a maximum budget constraint. The multi-objective function of the model maximizes the effectiveness of the campaign while minimizing the “broadness” of the targeted segments, allowing the detection of the most effective and homogeneous target group possible for a commercial action within a set of <span><math><mi>N</mi></math></span> continuous variables. The methodology performance was benchmarked against traditional customer clustering and greedy segmentation algorithms. The experiments were carried out in (1) simulated data environments and (2) based on real campaign information. The compared scenarios show that the proposed methodology outperforms the baseline model, the complexity of the problem scales non-linearly, increasing the number of variables, and the model increases 54% the effectiveness of a campaign without an increment in the segment range.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110945"},"PeriodicalIF":6.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small and medium-sized enterprise dedicated knowledge exploitation mechanism: A recommender system based on knowledge relatedness","authors":"Xingyu Sima ,&nbsp;Thierry Coudert ,&nbsp;Laurent Geneste ,&nbsp;Aymeric de Valroger","doi":"10.1016/j.cie.2025.110941","DOIUrl":"10.1016/j.cie.2025.110941","url":null,"abstract":"<div><div>Knowledge is a vital asset for organizations, especially in today’s Industry 4.0 context with the ever-increasing amount of information being produced. Organizations must consider knowledge management (KM) to create a sustainable competitive advantage. Currently, KM is applied relatively well in large organizations; however, small and medium-sized enterprises (SMEs) encounter various constraints. Knowledge exploitation is a key phase in KM for the retrieval of relevant knowledge. Therefore, a recommender system (RS), which is a promising and widely used information technology (IT) tool, is proposed in this study, for SMEs to enable effective knowledge exploitation. The RS can be adapted to SME KM specificities and a dedicated RS based on knowledge relatedness derived from different information sources is proposed herein. The proposed RS enables the recommendation of knowledge item balancing: i) historical application data, that is, information regarding how items were related during past projects, and ii) initial relatedness knowledge, which represents the relationships between knowledge items defined by knowledge experts. The proposed RS was developed in collaboration with the Axsens-bte SME, who specialize in consultancy and training in the supply chain, Industry 4.0, and quality requirements management. The proposed RS improved SME KM processes and increased efficiency in terms of exploiting knowledge assets. This demonstrated the ability of the proposed RS to assist SMEs in efficiently and effectively navigating complex information environments.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110941"},"PeriodicalIF":6.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}